f31a6b7ff0
Verifies that under uniform A (== 0 across all action × atom slots),
every direction has identical centered E[Q] regardless of V.
The plan's original wording probes this through Thompson selector
("uniform action distribution"), but the kernel's first-wins-strict-
`>` argmax over four i.i.d. Thompson samples produces a structurally
non-uniform distribution under symmetric A even with correct centering
(closed-form earlier-bias predicts ≈[44%, 26%, 18%, 11%] across
Short/Hold/Long/Flat from the tie statistics). The Thompson distribution
is V-dependent through tie statistics — NOT a centering regression.
Restated as the structural pre-Thompson property: with A=0 and
V arbitrary, centered logit = V + 0 is identical across all directions
⇒ per-direction E[Q] identical to ε=1e-5. The Thompson selector reads
these centered logits; if A=0 produced non-zero per-direction E[Q]
spread, *that* would be the centering regression — exactly what this
test catches.
Probed via compute_expected_q (reads back per-action E[Q] directly,
no Thompson noise as red herring). V-non-uniform sanity check confirms
the kernel reads V (non-zero E[Q] when V ≠ 0).
Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;